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Öğe A closed vendor managed inventory system under a mixed fleet of electric and conventional vehicles(Pergamon-Elsevier Science Ltd, 2021) Soysal, Mehmet; Belbag, Sedat; Sel, CagriIn a closed-loop supply chain, where a Vendor Managed Inventory system is executed, the Closed-Loop Inventory Routing Problem is one of the main problems confronted by logistics decision-makers. This study addresses a Closed-Loop Inventory Routing Problem under a mixed fleet of electric and conventional vehicles. The problem is formulated as a Mixed-Integer Linear Programming model and a Fix&Optimize algorithm is developed to tackle larger problem instances. The proposed decision support models incorporate comprehensive estimation approaches for energy consumption of both electric and conventional vehicles that allow to better estimate fuel and electric cost and transportation emissions. The models respect uncertain reverse returnable transport items flow from customers as well. The numerical analyses demonstrate the benefits that could be obtained by means of the provided models. The Fix&Optimize heuristic yields in 5.72% lower costs within 59.23% shorter computation times on average compared to the Mixed-Integer Linear Programming model. The proposed models are capable to provide trade-off analyses for sustainable logistics management.Öğe A cutting stock problem in the wood products industry: a two-stage solution approach(Wiley, 2022) Kokten, Erkan Sami; Sel, CagriIn this study, a cutting stock problem is addressed to determine the width/length of the wooden boards and select lumber in standard lengths for cutting a cable spool. A nonlinear mathematical model is introduced using Pythagoras' theorem. The aim is to minimize the total length of lumber used and equivalently the total amount of wood wasted. To reduce the computational burden, the mathematical model is decomposed into two submodels for sizing and cutting decisions, and a two-stage decomposition algorithm is proposed for solving the submodels subsequently. A simulated annealing metaheuristic combining the first-fit decreasing and increasing techniques (SA-FFD/I) is proposed to show the computational efficiency of the decomposition approach. The savings on the total length of lumber used and the total amount of wood wasted in production are achieved by the decomposition algorithm, which is 8% and 86.4% on average compared to the SA-FFD/I heuristic. Accordingly, a numerical analysis is conducted on a real case to assess how capacity load and demand pattern scenarios impact the solution. The ratio between the total amount of wood waste and the total length of lumber does not exceed 2.54% for a weekly planning horizon.Öğe Energy-aware production lot-sizing and parallel machine scheduling with the product-specific machining tools and power requirements(Pergamon-Elsevier Science Ltd, 2024) Sel, Cagri; Gurkan, M. Edib; Hamzadayi, AlperThis study addresses a multi-product lot-sizing and scheduling problem with sequence-dependent setup times, considering that the machining operations cause energy consumption. The production facility comprises identical parallel machines under which the production of each product requires a certain set of tools. The energy requirement of production depends on the product-specific machining tools. The problem deals with determining the minimum cost lot-sizing and scheduling plan considering the energy capacity of the production facility. We formulate the problem as a mixed integer linear programming model by introducing energy consumption-related costs and constraints. We perform a case study on CNC milling and turning workshops. Further, we propose an heuristic approach combining a decomposition-based Simulated Annealing heuristic and Fix&Optimise algorithms to handle larger-sized problem instances. The computational performance of the proposed heuristic approach is evaluated against the proposed mixed integer linear programming model on a numerical study. Our numerical experiments reveal that the proposed heuristic approach is capable of providing cost-efficient solutions without compromising time efficiency.Öğe A green model for the catering industry under demand uncertainty(Elsevier Sci Ltd, 2017) Sel, Cagri; Soysal, Mehmet; Cimen, MustafaIn this study, we design a catering supply chain involving production and service management. The production management involves food production lot-sizing and delivery scheduling decisions, while the service management is associated with balancing the food service lines. If production is in excess of demand, the food waste occurs along the catering chain. If the quantity produced is less than demand, this leads to a shortage. To address this problem, we develop a stochastic programming model accounting for the key performance indicators of total waste, total shortage and total cost of production and distribution. The selected indicators enable to assess the sustainability performance of the catering supply chain. The numerical study reflects real settings from catering operations of a university cafeteria in Turkey. For a sustainable development of catering supply chains, the analyses reveal the potential benefits of outsourcing (e.g., decreasing total costs by 36% and waste costs by 23%), employing qualified staff and increasing capacity through process improvement. (C) 2017 Elsevier Ltd. All rights reserved.Öğe A heuristic approach for green vehicle routing(Edp Sciences S A, 2021) Soysal, Mehmet; Cimen, Mustafa; Sel, Cagri; Belbag, SedatThis paper addresses a green capacitated vehicle routing problem that accounts for transportation emissions. A Dynamic Programming approach has been used to formulate the problem. Although small-sized problems can be solved by Dynamic Programming, this approach is infeasible for larger problems due to the curse of dimensionality. Therefore, we propose a Dynamic Programming based solution approach that involves the ideas of restriction, simulation and online control of parameters to solve large-sized problems. The added values of the proposed decision support tool have been shown on a small-sized base case and relatively larger problems. Performance comparisons of the proposed heuristic against other existing Dynamic Programming based solution approaches reveal its effectiveness, as in most of the instance-setting pairs, the proposed heuristic outperforms the existing ones. Accordingly, the proposed heuristic can be used as an alternative decision support tool to tackle real routing problems confronted in sustainable logistics management.Öğe INVIGILATORS ASSIGNMENT IN PRACTICAL EXAMINATION TIMETABLING PROBLEMS(Univ Cincinnati Industrial Engineering, 2022) Cimen, Mustafa; Belbag, Sedat; Soysal, Mehmet; Sel, CagriThis paper addresses an invigilator assignment problem. The problem deals with a set of exams, each of which requires a given number of invigilators. The aim is to prepare a conflict-free schedule where all invigilator requirements of the exams are met. In this study, the conflict-free schedule is determined by a mixed-integer linear programming model and a heuristic algorithm that accounts for the following real-life concerns; assignment of the invigilators responsible for an exam, reduction of the number of successive invigilation duties, fair distribution of total workload and unfavorable workload among invigilators and, prioritization of the assignments based on invigilators??? profession. The applicability of the proposed model and the heuristic algorithm has been shown on eight different real-life problems of leading public universities in Turkey and further eight larger-sized examinations set up based on the real settings. In universities, the real schedules are manually prepared by a faculty team. Compared to the assignment of the faculty team responsible for the examination of timetabling in which balancing only the numbers of duties, we achieved to 86% decrease in the total positive error of invigilation hours by fairly distributing the invigilation duties in the model results. Besides, the following improvements are achieved by applying the proposed model; a 56% decrease in the total number of successor assignments, a 44% decrease in the total unfavorable time, and a 23% increase in the total number of department-based assignments. The heuristic algorithm improves the team schedules by 4% in terms of the total positive error of total invigilation hours and 57% in terms of the total number of successive exam assignments. Accordingly, the proposed model and the heuristic algorithm can be used as a decision support tool by the faculty team.Öğe Modeling a closed-loop inventory routing problem for returnable transport items under horizontal logistics collaborations and dynamic demand structure(Taylor & Francis Ltd, 2024) Yavrucu, Erencan; Soysal, Mehmet; Sel, Cagri; Cimen, Mustafa; Hamzadayi, AlperThis paper addresses a closed-loop inventory routing problem with multiple suppliers, products, and periods under horizontal collaboration assumptions. Our problem encompasses various decision aspects, including routing, inventory management, product delivery, returnable transport item collection and cleaning. We analyze various logistics collaboration scenarios. The effects of demand dynamicity are also assessed. The problem has been mathematically defined as a Mixed Integer Linear Programming model. A rolling horizon approach and a hybrid heuristic algorithm are proposed for instances that exceed the computational requirements of solving the exact MILP model. The applicability and potential benefits of the MILP model and the proposed solution methodologies are demonstrated through a base case and additional numerical analyses on larger-sized instances and networks. The results show that supplier collaboration significantly reduces routing costs, while customer collaboration reduces inventory costs. Numerical comparisons reveal that the proposed algorithms outperform the MILP model for large-scale problem instances.Öğe A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem(Mdpi, 2023) Inal, Ali Firat; Sel, Cagri; Aktepe, Adnan; Turker, Ahmet Kursad; Ersoz, SuleymanIn a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New, unexpected events disrupt the production schedule and require dynamic scheduling updates to the production schedule on an event-based basis. To solve the dynamic scheduling problem, we propose a multi-agent system with reinforcement learning aimed at the minimization of tardiness and flow time to improve the dynamic scheduling techniques. The performance of the proposed multi-agent system is compared with the first-in-first-out, shortest processing time, and earliest due date dispatching rules in terms of the minimization of tardy jobs, mean tardiness, maximum tardiness, mean earliness, maximum earliness, mean flow time, maximum flow time, work in process, and makespan. Five scenarios are generated with different arrival intervals of the jobs to the job shop production system. The results of the experiments, performed for the 3 x 3, 5 x 5, and 10 x 10 problem sizes, show that our multi-agent system overperforms compared to the dispatching rules as the workload of the job shop increases. Under a heavy workload, the proposed multi-agent system gives the best results for five performance criteria, which are the proportion of tardy jobs, mean tardiness, maximum tardiness, mean flow time, and maximum flow time.Öğe Optimizing food logistics through a stochastic inventory routing problem under energy, waste and workforce concerns(Elsevier Sci Ltd, 2023) Koseli, Ilker; Soysal, Mehmet; cimen, Mustafa; Sel, CagriThe trend towards sustainable operations management makes it inevitable for companies to carry out their operations by considering environmental and social externalities. This tendency has implications also in the food logistics industry. This study addresses a single-period closed Inventory Routing Problem under environmental and social sustainability concerns in daily food logistics systems. In particular, the study focuses on reducing CO2 emissions in a refrigerated transportation system, collecting and disposing of waste, and offering employees more enticing work schedules, that have not been simultaneously addressed in the literature. The problem has been mathematically defined as a Mixed Integer Linear Programming model. A solution approach based on a clustering algorithm has been proposed to solve large-sized cases. The applicability of the proposed decision support models and the potential practical benefits obtained from their use are shown by performing numerical analyses on an industrial case and a set of larger instances. The results show that simultaneously respecting workforce constraints, waste collection/disposal, and demand uncertainty provide improved economic, environmental and social outputs for food logistics companies. Due to workforce constraints, the delivery time is shortened by 5.5 h, which allow the manufacturing to start later. Respecting waste collection and disposal as well as demand uncertainty enables cost reductions of %40.9 and %6, respectively.Öğe Planning and scheduling of the make-and-pack dairy production under lifetime uncertainty(Elsevier Science Inc, 2017) Sel, Cagri; Bilgen, Bilge; Bloemhof-Ruwaard, JacquelineIn the dairy processing, the rapid quality decay of milk-based intermediate mixture to make and pack restricts productivity and, forces organizations to carefully plan and schedule their production. Hereby, in this study, we consider a planning and scheduling problem encountered in the dairy industry and propose a chance-constrained programming model accounting for uncertainty in quality decay of intermediate mixture. The aim of the model is to find the optimal lot sizes and production schedule with minimum makespan (total time needed to finish the daily production). The proposed schedule allows the storage duration of intermediate mixture to be within a stochastic lifetime. A case study is presented to illustrate the typical structure of the two-stage semi-continuous make-and-pack production process. The numerical study reflects real settings from a set (Balkan type) yoghurt production process. Accordingly, a simulation of the production process is introduced to evaluate the proposed production plan and schedule in terms of product waste. The model is examined with 32 scenarios consisting of different distribution parameters, confidence levels and demand patterns. Overall in the scenarios, the proposed plan and schedule result in decreasing 26931 of product waste with 3.24 h increase of makespan in total. (C) 2017 Elsevier Inc. All rights reserved.Öğe A simulated annealing approach based simulation -optimisation to the dynamic job-shop scheduling problem(Pamukkale Univ, 2018) Sel, Cagri; Hamzadayi, AlperIn this study, we address a production scheduling problem. The scheduling problem is encountered in a job-shop production type. The production system is discrete and dynamic system in which jobs arrive continually. We introduce a simulation model (SM) to identify several situations such as machine failures, changing due dates in which scheduling rules (SRs) should be selected independently. Three SRs, i.e. the earliest due date rule (EDD), the shortest processing time first rule (SPT) and the first in first out rule (FIFO), are incorporated in a SM. A simulated annealing heuristic (SA) based simulation-optimisation approach is proposed to identify the unknown schedules in the dynamical system. In the numerical analysis, the performance of SRs and SA are compared using the simulation experiments. The objective functions minimising the mean flowtime and the mean tardiness are examined with varying levels of shop utilization and due date tightness. As an overall result, we observe that the proposed SA heuristic outperforms EDD and FIFO, the well-known SPT rule provides the best results. However, SA heuristic achieves very close results to the SPT and offers a reasonable computational burden in time-critical applications.Öğe Sustainability analysis of the use of natural gas in the iron and steel industry(Springer Heidelberg, 2023) Balli, Mucahid Fatih; Sel, CagriIn this study, using natural gas instead of coke gas in the reheating furnace in a steelmaking company is investigated in terms of economic, social, and environmental impacts. A sample projection is prepared, and economic analyses are conducted in line with the production plan target for future planning periods of 144 months. The natural gas usage increases the production quantity by 914 tonnes and allows the company to produce 5,979,334 kWh of additional electric power from the metallurgical gases monthly. In the economic analysis, we use engineering economics techniques to examine the economic impacts of the modernization investment for reheating furnaces. Accordingly, a positive return for each month shows the feasibility of the renovation project. The self-paying time of the investment is calculated as a short period of 11 months. Besides, the social and environmental impacts are notable; the renovation project decreases occupational health and safety risks by using natural gas as a substitute fuel, preventing a potential explosion or poisoning risk in the production, storage, and distribution. The renovation project decreases the global warming potential of blast furnace gas constituents and carbon emissions by 0.84% per month.Öğe The use of parametric programming and simulation-optimisation approaches for stochastic inventory control in the food markets under fuzzy deterioration rate(Pergamon-Elsevier Science Ltd, 2022) Sel, CagriIn this study, we address the stochastic inventory control problem of green vegetables deteriorating in a fruit and vegetable wholesaler. The problem is to re-order green vegetables periodically to meet demand of the green markets. The aim is to decide the order quantity and the order-up-to level for each period minimising the total inventory cost. In the fruit and vegetable wholesaler, a staff separates the fresh and non-fresh products to sustain freshness in the green vegetable crates. The behaviour of the wholesaler staff on selecting the non-fresh green vegetables is personal reasoning that fuzzy logic can help to deal with the uncertainty. Accordingly, we introduce a stochastic integer linear programming model accounting for the uncertain demand. We represent the deterioration rate indicated in the model by fuzzy numbers and transform the resulting fuzzy model into an alpha-para-metric programming model. As a solution method, we propose a simulated annealing based simulation optimisation algorithm. The models and the heuristic algorithm are applied to a case study that reflects the real settings of a food market in Turkey. The numerical analysis is conducted on the case study to understand the effect of the fuzziness on the solution quality and time under varying cost parameters and deterioration rates. As a result, the numerical analyses demonstrate that the proposed models allow us better to estimate the total costs and the waste quantity. The heuristic algorithm yields near-optimal solutions. The solutions approach approximately 3% difference on average for the medium and long term decisions. The heuristic algorithm results in significantly shorter computation times compared to the proposed model.
